Computational Science and Engineering

Field of Specialisation:

Chemistry and Biology

Contact Person:

Prof. Philippe Hünenberger

MOLECULAR MODELING/SIMULATION IN CHEMISTRY & BIOLOGY

Understand (and predict) the workings of (bio)molecularsystems using physics-based models

Simulation can replace

of experiment when:

Simulation can complement

experiment when:

• the process cannot be studied experimentallye.g. interior of a star,

weather forecast (experiment is too late !)

• the process is dangerous to study experimentallye.g. flight simulators,

explosion of a nuclear bomb, fighting ability of the Swiss army

• the process is expensive to study experimentallye.g. volcanism on Venus,

aerodynamics in aircraft design

• approximate simulations reduce thenumber of experiments to be performedor/and increase their likelihood of successe.g. modeling in industry:

drug design, protein engineering, stock market predictions (banks),risk assessment (insurances)

• a simulation reproducing an experimentprovides additional insighte.g. modeling in academia:

quantum chemistry,molecular simulations

• very simple ⇒ analytical treatment (e.g. perfect gas, harmonic crystal)• moderately complex but numerous ⇒ computer simulation (numerical solution)• not known or too complex ⇒ small-scale simulation (e.g. avalanches)

The equations governing

the model may be:

e.g. experimentally inaccessibleresolution in time/space/energy

MOLECULAR

MODEL

degrees of freedom

interaction boundary conditions

generation of configurations

system size and shape,thermodynamical constraints,

experimentally-derivedinformation

FOUR BASIC CHOICES DEFINING A MOLECULAR MODEL

Hamiltonian operatoror function

number of configurations,properties of the

configuration sequence

elementary “particles”of the model

Levels of modelling, resolution and degrees of freedom

QUANTUM MODELS CLASSICAL MODELS MESOSCOPIC MODELS

lowest

resolution

highest

resolution

IMPLICIT

nucleons

core electrons,

high energy photons

all electrons, medium

energy photons

(Born-Oppenheimer)

solvent

nuclei (→ atoms),

all photons

IMPLICIT

(non-polar)

hydrogen

(→united atoms)

atom groups

(→beads)

solvent

atom groups

(→residues)

IMPLICIT

freely inspired from

"Simulating the physical world"

by Herman Berendsen (2007)

intramolecular

dof

(→molecules)

intramolecular

dof

(→"particles")

granularity

of matter

(→ densities,

fluxes and fields)

QUANTUM

CHEMISTRY

QUANTUM

CHEMISTRY

(IMPL. SVT.)

MOLECULAR

MECHANICS

COARSE-

GRAINED

MODELS

MOLECULAR

MECHANICS

(IMPL. SVT.)

rel. TDSE (Dirac)

TDSE

TISE (elec.)

TDSE (nucl.)

TISE (elec.)

TDSE (nucl.)

RESIDUE-

BASED

MODELS

=

MD

MD

MD

SD

SD

BD, DPD

FE (conserv. + transp.)

RIGID-

MOLECULE

MODELS

MESOSCOPIC

MODELS

CONTINUUM

MODELS

MD, SD

QUANTUM

MECHANICS

QUANTUM

MECHANICS

MOLECULAR

MECHANICS

(UNITED ATOM)

MOLECULAR MODELING/SIMULATION IN CHEMISTRY & BIOLOGY

DEGREES OF FREEDOM: FROM QUANTUM TO MESOSCOPIC MODELS

increasing resolutionand Hamiltonian cost

increasing system size and number of configurations

QUANTUM MODELS

CLASSICAL MODELS

MESOSCOPIC MODELS

currentlynot feasible

FASTERCOMPUTERS

• Levels of resolution: the tradeoff

• Computing power: Moore's law

1960 1970 1980 1990 2000

year

6

9

12

log[flo

p]

IBM 7090

CDC 6000

IBM 360/195

FUJITSU VPP

CRAY T3DSX3

FUJITSU VP 200

CRAY 2

NEC SX 2

CDC 7600

CRAY−1 CYBER−205

CRAY X−MP

megaflop

gigaflop

teraflop 1 flop = 1 floating-point operation (14 digit precision) per second

The computing power has increased till nowon average by a factor 10 every 6 years

milliflop…

EXAMPLE: CLASSICAL SIMULATION

• Gel (GL) – liquid crystal (LC) phase transition in GMP bilayers

mT

GL LC

Glycerolmonopalmitate

(GMP)

• Experimentally, these lipids evidence the

usual Tm increase upon dehydration

• Can we calculate (bracket) their

Tm as a function of hydration ?

Experimentalphase diagram

of GMP

• Simulations of a 2×32 bilayer patch using GROMOS 53AOXY and SPC water

→ Full (F), half (H) or quarter (Q) hydration

→ Starting from liquid-crystal (LC) or gel (GL) phase

→ At various temperatures differing by 4K

F

H

Q

EXAMPLE: CLASSICAL SIMULATION

Horta, de Vries & Hünenberger

J. Chem. Theory Comput. 6 2488 (2008)

[+ Laner & Hünenberger, Mol. Simul., in press]

Exp: 50, 53 and 58oCfor F, H and Q (?±±±±4oC?)

Sim: 51, 59 and 63oCfor F, H and Q (±±±±2oC)

→ investigate further: effect of lipid type & chirality, effect of added

cosolutes such as alcohols (anesthesia) or sugars (bioprotection)

EXAMPLE: QUANTUM-MECHANICAL/CLASSICAL SIMULATION

• QM/MM simulations of HIV-protease in aqueous solution Liu, Müller-Plathe & van Gunsteren

J. Mol. Biol. 261 (1996) 454-469

EXAMPLE: QUANTUM-MECHANICAL/CLASSICAL SIMULATION

CH2

COO-

H2C

COOH

HC

NH

CHC

H2O

O

MM

QM

active site

substrate

protein water

QM: Semi-empirical PM3-model at each time point

MD: Newton’s equationsof motion at 300K

65000 degrees of freedom

periodic box 5.1*5.3*7.2 nm3

ASP-25’ ASP-25

dimer,

2*99 residues

≈ 2000 atoms

GROMOS force field

( )U m−∇ = = ɺɺr F r ˆ ( ) ( )H EΨ = Ψr r

Computing Effort

proportional to N1-2atoms

Computing Effort

proportional to N3-5electrons

5427

SPC model

liquid alkanes: hexadecane

Compare: - structural characteristics

- energetic / entropic characteristics

MAP

“mapped”

all-atom

configurations

CG

Coarse-grained model

4 atoms

W

Centre of mass A1 – A4

Centre of mass B1 – B4

Centre of mass C1 – C4

Centre of mass D1 – D4

A

B

C

D

AL(FG)

All-atom model

(non-hydrogen)

16 (CH2 or CH3) atoms

simulation + analysis simulation analysis

EXAMPLE: COARSE-GRAINED SIMULATION

Marrink et al., J. Phys.Chem.B 108 (2004) 750

Multi-grained simulation of 25 hexadecanes in water

Time: 0 ps 8ps 25ps 100ps

8.5ps 25.5ps

CG + FG CG CG CG + FG

FG FG

CG level simulation with occasional switching to FG level enhances exploration of FG conformational space

Interactions at CG and FG levels should be thermodynamically consistent

EXAMPLE: COARSE-GRAINED SIMULATIONChristen & van Gunsteren

J. Chem. Phys, 124 (2006) 154106

Bachelor Studium

Vorlesung SWS Semester Departement KP

- Computer Simulation in Chemistry, Biology and Physics 3G HS (7-th) CHAB 7

- Quantum Chemistry 3G FS (5-th) CHAB 6

Master Studium

Vorlesung SWS Semester Departement KP

- Computer simulation in Chemistry, Biology and Physics 3G HS (7-th) CHAB 7

- Quantum Chemistry 3G FS (5-th) CHAB 6

- Advanced Quantum Chemistry 3G HS (7-th) CHAB

- Computational Biology 3V 2U HS INFK 6

- Computer Applications: Finite Elementsin Solids and Structures 2V 2U FS MATL 4

- Seminar in Chemistry and Biology HS/FS RW 4

Vertiefungsgebiet-Vorlesungen: Chemie und Biologie

Hünenberger

Reiher

Hünenberger

Reiher

Reiher

Gonnet

Gusev

Hünenberger